With the advent of digital communication, wireless networks have been effectively adopted worldwide for facilitating various networking applications. For example, wireless communication has become the de-facto communication medium at homes, work locations, malls, airports, stadiums, colleges, and other necessary mediums. Further, due to gradual growth in miniaturization technology, dimensions of battery operated handheld devices, or more specifically, the nodes in general of a wireless network have been drastically reduced in order to serve variety of applications by means of these handheld devices.
In general, to execute the applications by means of wireless communication these nodes require high rate of transmission. Additionally, these nodes need to support long periods of use between battery charges. Thus, the lifetime of the nodes becomes crucial and therefore these nodes require enhanced lifetime and higher rate of communication. In order to facilitate the higher transmission rate and enhance the lifetime of the wireless network, optimal techniques are required.
In the present scenario, maximization of lifetime and throughput requires frequent exchange of control messages between nodes. However, since wireless networks are broadcasting networks, such control message exchanges not only reduce the effective rate of transmission between nodes, but also impact the overall lifetime of the nodes. This is because wireless nodes are mainly battery powered and processing of a packet/frame or message for transmission or reception requires energy consumption. Therefore, while optimal techniques are important, they also consume resources in terms of battery and throughput.
Wireless networks in general are of two types: centralized network and distributed network. In a centralized network, each wireless node communicates with a central node or base station for usual communication. However, in a distributed network, each wireless node communicates with other nodes or its neighbors for computing, communication, storage and other services in a distributed fashion. Therefore, in order to transmit at its optimal rate each node requires control messages to communicate with its neighboring node. Since the wireless network is broadcasting in nature, inter node communications not only hamper the effective rate or throughput of the network, but also reduce the lifetime of the network. In addition to this, the complexity of communication protocols and computation also reduces the lifetime of the network. More specifically, the optimization techniques used in the background require message passing between the individual nodes. In other terms, each node requires adequate information regarding the power available in the neighboring nodes, interference in other nodes, and maximum capacity, etc. Further, in order to share this information with the nodes, it is evident that more resources in terms of battery power and throughput are required. Finding an optimal solution for the above problem in real time is very difficult and often ends up with an iterated solution. Therefore, it demands more computing power. Thus, as these methods consume more battery power, hence they may not be feasible for optimization of the wireless networks.
Thus, the existing practices of achieving enhancement in lifetime and throughput are complex and difficult to implement. Most of the existing approaches require message passing which is resource consuming and hence are non-scalable. Further, in the existing optimal approaches, each node requires detailed information about the other nodes in the network. Such detail information includes placement of each node in the network, power available, detailed QoS requirement, etc.
Therefore, in view of the above, there is a long-felt need in the art for a method and system that enables enhancement of lifetime and throughput of wireless network that is scalable, resource-saving, less complex and easy to deploy at each node. More specifically, there is a need for a method and system that avoids passing of control messages amongst the individual nodes in the network and thereby facilitates the self-optimization of the network.